Robot-assisted radical prostatectomy in the Middle East: A report on the perioperative outcomes from a tertiary care centre in Lebanon

Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study

Purpose

This study assesses the knowledge and attitudes of medical students in Lebanon toward Artificial Intelligence (AI) in medical education. It also explores the students' perspectives regarding the role of AI in medical education as a subject in the curriculum and a teaching tool.

Methods

This is a cross-sectional study using an online survey consisting of close-ended questions. The survey targets medical students at all medical levels across the 7 medical schools in Lebanon.

Results

A total of 206 medical students responded. When assessing AI knowledge sources (81.1%) got their information from the media as compared to (9.7%) from medical school curriculum. However, Students who learned the basics of AI as part of the medical school curriculum were more knowledge about AI than their peers who did not. Students in their clinical years appear to be more knowledgeable about AI in medicine. The advancements in AI affected the choice of specialty of around a quarter of the students (26.8%). Finally, only a quarter of students (26.5%) want to be assessed by AI, even though the majority (57.7%) reported that assessment by AI is more objective.

Conclusions

Education about AI should be incorporated in the medical school curriculum to improve the knowledge and attitudes of medical students. Improving AI knowledge in medical students will in turn increase acceptance of AI as a tool in medical education, thus unlocking its potential in revolutionizing medical education.

Short-term oncological and surgical outcomes of robot-assisted radical prostatectomy: A retrospective multicenter cohort study in Japan (the MSUG94 group)

INTRODUCTION

We evaluated oncological outcomes of patients undergoing robot-assisted radical prostatectomy (RARP) for prostate cancer (PCa) and their perioperative complications in Japan. We investigated clinical and pathological covariates to predict biochemical recurrence (BCR) after RARP.

METHODS

A retrospective multicenter cohort study was conducted in RARP patients with PCa at 10 institutions in Japan. Pre- and postoperative covariates were collected from enrolled patients. The primary endpoint was defined as biochemical recurrence-free survival (BRFS). Additionally, the association between BCR and clinicopathological covariates was determined.

RESULTS

We enrolled 2670 patients in this study. The median follow-up period was 26.0 months. RARP-related perioperative complications were identified in 198 patients (7.4%), including 69 patients (2.6%) with grade 3/4 complications according to the Clavien-Dindo classification. The 2-year BRFS was 88.0%. Using the Kaplan-Meier method, initial prostate-specific antigen (PSA) level of ≤7.6 ng/mL, biopsy and pathological Gleason score (GS) of ≤7, clinical and pathological T1/2, and low/intermediate risks according to the National Comprehensive Cancer Network risk classification, and negative surgical margin status had significant BRFS than their counterparts. In multivariate analysis, initial PSA, biopsy and pathological GS, clinical and pathological T stage, and surgical margin status significantly correlated with BCR after RARP.

CONCLUSION

In this study, RARP achieved a lower incidence of perioperative complications than other studies.

Patient characteristics predicting prolonged length of hospital stay following robotic-assisted radical prostatectomy

Objective:

The objective of this study is to determine the preoperative patient characteristics predicting prolonged length of hospital stay (pLOS) following robotic-assisted radical prostatectomy (RARP).

Methods:

The National Surgical Quality Improvement Program (NSQIP) database was used to select patients who underwent RARP without other concomitant surgeries between 2008 and 2016. Patients’ demographics, comorbidities, and laboratory markers were collected to evaluate their role in predicting pLOS. The pLOS was defined as length of stay (LOS) >2 days. A multinomial logistic regression was constructed adjusting for postoperative surgical complications to assess for the predictors of pLOS.

Results:

We obtained data for 31,253 patients of which 20,774 (66.5%) patients stayed ⩽1 day, 6993 (22.4%) patients stayed for 2 days, and 3486 (11.2%) patients stayed for >2 days. Demographic variables – including body mass index (BMI) <18.5: odds ratio (OR) = 2.8, 95% confidence interval (CI) = [1.7–4.8]; smoking: OR = 1.2, 95% CI = [1.1–1.4]; and dependent functional status: OR = 3.1, 95% CI = [1.6–6.0] – were predictors of pLOS. Comorbidities – such as heart failure: OR = 4.6, 95% CI = [2.0–10.8]; being dialysis dependent: OR = 2.7, 95% CI = [1.4–5.0]; and predisposition to bleeding: OR = 2.0, 95% CI =  [1.5–2.7] – were the strongest predictors of extended hospitalization. In addition, pLOS was more likely to be associated with postoperative bleeding, renal, or pulmonary complications.

Conclusion:

Preoperative patient characteristics and comorbidities can predict pLOS. These findings can be used preoperatively for risk assessment and patient counseling.